Dynamics identification and forecasting of COVID-19 by switching Kalman filters

Zeng, Xiaobo; Ghanem, Roger

doi:10.1007/s00466-020-01911-4

Cited by 20 publications

(18 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kalman Filter (KF) is a widely used method for tracking and navigation and filtering and time series (Zeng and Ghanem 2020 ). The problem of Monitoring Outbreak spreading is pertinent to the control of morbidity.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Modeling the progression of COVID-19 deaths using Kalman Filter and AutoML

Han

Gois²,

Oliveira³

et al. 2021

Soft Comput

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Modeling the progression of COVID-19 deaths using Kalman Filter and AutoML

Han

Gois²,

Oliveira³

et al. 2021

Soft Comput

View full text Add to dashboard Cite

“…Several traditional approaches based on switching Kalman filters, Gaussian regression, Susceptible-Infected-Removed (SIR) models, have been proposed for forecasting the spread, confirmed and recovery cases due to COVID-19 virus in [12] , [13] , [14] . A susceptible–exposed–infected–removed (SEIR) model was trained for forecasting the spread of COVID-19 in China using data acquired between January 10 to February 15, 2020 for training, and February 16 to March 3, 2020 for testing, in [15] .…”

Section: Review Of Related Workmentioning

confidence: 99%

Leveraging weather data for forecasting cases-to-mortality rates due to COVID-19

Iloanusi

Ross

2021

Chaos, Solitons & Fractals

View full text Add to dashboard Cite

There are several recent publications criticizing the failure of COVID-19 forecasting models, with swinging over predictions and underpredictions, which have made it difficult for decision and policy making. Observing the failures of several COVID-19 forecasting models and the alarming spread of the virus, we seek to use some stable response for forecasting COVID-19, viz., ratios of COVID-19 cases to mortalities, rather than COVID-19 cases or fatalities. A trend of low COVID-19 cases-to-mortality ratios calls for urgent attention: the need for vaccines, for instance. Studies have shown that there are influences of weather parameters on COVID-19; and COVID-19 may have come to stay and could manifest a seasonal outbreak profile similar to other infectious respiratory diseases. In this paper, the influences of some weather, geographical, economic and demographic covariates were evaluated on COVID-19 response based on a series of Granger-causality tests. The effect of four weather parameters, viz., temperature, rainfall, solar irradiation and relative humidity, on daily COVID-19 cases-to-mortality ratios of 36 countries from 5 continents of the world were determined through regression analysis. Regression studies show that these four weather factors impact ratios of COVID-19 cases-to-mortality differently. The most impactful factor is temperature which is positively correlated with COVID-19 cases-to-mortality responses in 24 out of 36 countries. Temperature minimally affects COVID-19 cases-to-mortality ratios in the tropical countries. The most influential weather factor – temperature – was incorporated in training random forest and deep learning models for forecasting the cases-to-mortality rate of COVID-19 in clusters of countries in the world with similar weather conditions. Evaluation of trained forecasting models incorporating temperature features show better performance compared to a similar set of models trained without temperature features. This implies that COVID-19 forecasting models will predict more accurately if temperature features are factored in, especially for temperate countries. © 2017 Elsevier Inc. All rights reserved.

show abstract

“…Other similar researches include Abuhasel et al [ 1 , 28 , 29 ], Singh et al [ 28 , 29 ], and Yousaf et al [ 36 ]. The other linear methods include quantile regression [ 23 ] and Kalman Filter [ 28 , 29 , 37 ]. The second uses artificial intelligence methods, since the increase of confirmed cases is nonlinear.…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Trend of COVID-19 Considering the Impacts of Public Health Interventions: An Application of FGM and Buffer Level

Zhang

Chen

et al. 2021

J Healthc Inform Res

View full text Add to dashboard Cite

Purpose COVID-19 is still showing a tendency of spreading around the world. In order to improve the subsequent control of COVID-19, it is essential to conduct a study on measuring and predicting the scale of the outbreak in the future. Methods This paper uses rolling mechanism and grid search to find the best fractional order of Fractional Order Accumulation Grey Model (FGM). Buffer level is proposed based on the general form of weakening buffer operator to measure the effect of government control measurements on the epidemic. And the buffer level is associated with the Government Response Stringency index and the Mobility Index. Results Firstly, the model proposed in this paper dominates the ARIMA model which has been widely used in predicting the confirmed COVID-19 cases. Secondly, in the process of using the buffer level to modify the FGM, this paper finds that government measurements require the active cooperation of the public and often have a time lag when they are effective. Only when government increase its stringency and the public observe the order can the spread of COVID-19 be slowed down. If there is only the controlling measure and the public does not react actively, it will not slow down the epidemic. Thirdly, according to the Mobility Index and Government Response Stringency Index in December, this paper predicts the cumulative confirmed cases of the end of January in different scenarios according to different buffer levels. The study suggests that the world should continue to maintain high vigilance and take corresponding control measures for the outbreak of COVID-19. Conclusions Government’s control measures and public’s abidance are both important in this battle with COVID-19. Governments control measures have time-lag effect and the time lag is about 9 days. When the government increases its stringency and the public cooperates with the government, we must consider the weaken buffer operator with proper buffer level in the prediction process. These prediction methods can be considered in the prediction of COVID-19 confirmed cases in the future or the trend of other epidemics.

show abstract

Dynamics identification and forecasting of COVID-19 by switching Kalman filters

Cited by 20 publications

References 31 publications

RETRACTED ARTICLE: Modeling the progression of COVID-19 deaths using Kalman Filter and AutoML

RETRACTED ARTICLE: Modeling the progression of COVID-19 deaths using Kalman Filter and AutoML

Leveraging weather data for forecasting cases-to-mortality rates due to COVID-19

Forecasting the Trend of COVID-19 Considering the Impacts of Public Health Interventions: An Application of FGM and Buffer Level

Contact Info

Product

Resources

About